callback_async_fselect: Create Asynchronous Feature Selection Callback

In mlr3fselect: Feature Selection for 'mlr3'

Create Asynchronous Feature Selection Callback

Description

Function to create a CallbackAsyncFSelect. Predefined callbacks are stored in the dictionary mlr_callbacks and can be retrieved with clbk().

Feature selection callbacks can be called from different stages of the feature selection process. The stages are prefixed with ⁠on_*⁠.

Start Feature Selection
     - on_optimization_begin
    Start Worker
         - on_worker_begin
         Start Optimization on Worker
           - on_optimizer_before_eval
             Start Evaluation
               - on_eval_after_xs
                 Start Resampling Iteration
                   - on_resample_begin
                   - on_resample_before_train
                   - on_resample_before_predict
                   - on_resample_end
                 End Resampling Iteration
               - on_eval_after_resample
               - on_eval_before_archive
             End Evaluation
          - on_optimizer_after_eval
         End Optimization on Worker
         - on_worker_end
    End Worker
     - on_fselect_result_begin
     - on_result_begin
     - on_result_end
     - on_optimization_end
End Feature Selection

See also the section on parameters for more information on the stages. A feature selection callback works with ContextAsyncFSelect.

Usage

callback_async_fselect(
  id,
  label = NA_character_,
  man = NA_character_,
  on_optimization_begin = NULL,
  on_worker_begin = NULL,
  on_optimizer_before_eval = NULL,
  on_eval_after_xs = NULL,
  on_resample_begin = NULL,
  on_resample_before_train = NULL,
  on_resample_before_predict = NULL,
  on_resample_end = NULL,
  on_eval_after_resample = NULL,
  on_eval_before_archive = NULL,
  on_optimizer_after_eval = NULL,
  on_worker_end = NULL,
  on_fselect_result_begin = NULL,
  on_result_begin = NULL,
  on_result_end = NULL,
  on_result = NULL,
  on_optimization_end = NULL
)

Arguments

id	(character(1)) Identifier for the new instance.
label	(character(1)) Label for the new instance.
man	(character(1)) String in the format ⁠[pkg]::[topic]⁠ pointing to a manual page for this object. The referenced help package can be opened via method ⁠$help()⁠.
on_optimization_begin	(⁠function()⁠) Stage called at the beginning of the optimization. Called in Optimizer$optimize(). The functions must have two arguments named callback and context.
on_worker_begin	(⁠function()⁠) Stage called at the beginning of the optimization on the worker. Called in the worker loop. The functions must have two arguments named callback and context.
on_optimizer_before_eval	(⁠function()⁠) Stage called after the optimizer proposes points. Called in OptimInstance$.eval_point(). The functions must have two arguments named callback and context. The argument of instance$.eval_point(xs) and xs_trafoed and extra are available in the context. Or xs and xs_trafoed of instance$.eval_queue() are available in the context.
on_eval_after_xs	(⁠function()⁠) Stage called after xs is passed to the objective. Called in ObjectiveFSelectAsync$eval(). The functions must have two arguments named callback and context. The argument of ⁠$.eval(xs)⁠ is available in the context.
on_resample_begin	(⁠function()⁠) Stage called at the beginning of a resampling iteration. Called in workhorse() (internal). See also mlr3::callback_resample(). The functions must have two arguments named callback and context.
on_resample_before_train	(⁠function()⁠) Stage called before training the learner. Called in workhorse() (internal). See also mlr3::callback_resample(). The functions must have two arguments named callback and context.
on_resample_before_predict	(⁠function()⁠) Stage called before predicting. Called in workhorse() (internal). See also mlr3::callback_resample(). The functions must have two arguments named callback and context.
on_resample_end	(⁠function()⁠) Stage called at the end of a resampling iteration. Called in workhorse() (internal). See also mlr3::callback_resample(). The functions must have two arguments named callback and context.
on_eval_after_resample	(⁠function()⁠) Stage called after a feature subset is evaluated. Called in ObjectiveFSelectAsync$eval(). The functions must have two arguments named callback and context. The resample_result is available in the context.
on_eval_before_archive	(⁠function()⁠) Stage called before performance values are written to the archive. Called in ObjectiveFSelectAsync$eval(). The functions must have two arguments named callback and context. The aggregated_performance is available in context.
on_optimizer_after_eval	(⁠function()⁠) Stage called after points are evaluated. Called in OptimInstance$.eval_point(). The functions must have two arguments named callback and context.
on_worker_end	(⁠function()⁠) Stage called at the end of the optimization on the worker. Called in the worker loop. The functions must have two arguments named callback and context.
on_fselect_result_begin	(⁠function()⁠) Stage called at the beginning of the result writing. Called in ⁠FSelectInstance*$assign_result()⁠. The functions must have two arguments named callback and context. The arguments of ⁠$assign_result(xdt, y, extra)⁠ are available in context.
on_result_begin	(⁠function()⁠) Stage called at the beginning of the result writing. Called in OptimInstance$assign_result(). The functions must have two arguments named callback and context. The arguments of ⁠$.assign_result(xdt, y, extra)⁠ are available in the context.
on_result_end	(⁠function()⁠) Stage called after the result is written. Called in OptimInstance$assign_result(). The functions must have two arguments named callback and context. The final result instance$result is available in the context.
on_result	(⁠function()⁠) Deprecated. Use on_result_end instead. Stage called after the result is written. Called in OptimInstance$assign_result().
on_optimization_end	(⁠function()⁠) Stage called at the end of the optimization. Called in Optimizer$optimize().

Details

When implementing a callback, each function must have two arguments named callback and context. A callback can write data to the state (⁠$state⁠), e.g. settings that affect the callback itself. Feature selection callbacks access ContextAsyncFSelect and mlr3::ContextResample.

mlr3fselect documentation built on Aug. 8, 2025, 7:46 p.m.